Multi-Scale Spatial Feature Generation for Spatio-Temporal Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current spatio-temporal prediction systems fail to effectively utilize spatial data at different scales, as they cannot consider spatially sensitive factors across and within different spatial layers, leading to incomplete event predictions.
Innovation Solution
A method and system that generate new features based on spatial relationships between and within multi-scale spatial datasets, using spatial relationship matrices to select spatially sensitive features for training predictive models, allowing for event predictions across varying granularities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatial data at different scales is not integrated, then the prediction system is simpler to implement, but the prediction accuracy deteriorates due to incomplete spatially sensitive factors
Solution Approach 1:
The patent segments spatial data into multiple scales (e.g., city-level, neighborhood-level, street-level) and processes each scale separately through distinct processing pipelines. Spatial relationship matrices are generated for each scale independently, allowing the system to handle complex multi-scale data without overwhelming complexity by breaking it down into manageable segments.
Solution Approach 2:
The patent adds a spatial scale dimension to the prediction system by integrating data across multiple granularities. Instead of processing spatial data at a single level, the system incorporates vertical scaling (from coarse to fine granularity) as an additional dimension, enabling comprehensive capture of spatially sensitive factors while maintaining structured processing through hierarchical organization.
2Reliability
If spatial relationships across multiple scales are analyzed, then the predictive model captures more spatially sensitive factors, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary generation of spatial relationship matrices for each spatial scale before the main prediction process. By pre-computing these matrices that capture spatial relationships at different granularities, the system prepares the data structure in advance, reducing the computational burden during actual prediction while ensuring comprehensive spatial factor coverage.
Solution Approach 2:
The patent introduces spatial relationship matrices as intermediary structures that mediate between raw multi-scale spatial data and the predictive model. These matrices serve as intermediate representations that encode spatial relationships in a standardized format, simplifying the integration of complex multi-scale data into the prediction algorithm and reducing direct computational complexity.
3Loss of information
If features from multiple spatial scales are combined, then the feature set becomes more comprehensive, but the difficulty of selecting relevant features increases
Solution Approach 1:
The patent segments the feature selection process by scale, generating and evaluating features separately for each spatial granularity level. By organizing features according to their spatial scale origin and processing them through scale-specific selection criteria, the system manages the complexity of multi-scale feature integration while ensuring comprehensive information capture from each level.
Data Source
AI summary
A method and system to perform spatio-temporal prediction are described. The method includes obtaining, based on communication with one or more sources, multi-scale spatial datasets, each of the multi-scale spatial datasets providing a type of information at a corresponding granularity, at least two of the multi-scale spatial datasets providing at least two types of information at different corresponding granularities. The method also includes generating new features for each of the multi-scale spatial datasets, the new features being based on features of each of the multi-scale spatial datasets and spatial relationships between and within the multi-scale spatial datasets. The method further includes selecting, using the processor, features of interest from among the new features, training a predictive model based on the features of interest, and predicting an event based on the predictive model.